Exploring breast surgeons’ reasons for women not undergoing immediate breast reconstruction
Bibliographic record
Abstract
INTRODUCTION: Factors influencing breast reconstruction rates in Canada are complex and multi-factorial, ranging from patient-related to systemic considerations. For plastic surgeons, rates of immediate breast reconstruction (IBR) hinge on referral patterns from general surgeons performing breast cancer surgery and informed discussions with patients about their goals and risk tolerance. We seek to understand the reasons Alberta patients are not receiving IBR as reported by general surgeons. METHODS: The Synoptec™ database is a synoptic operative report designed by Cancer Surgery Alberta™ and utilized by 95% of Alberta breast cancer surgeons. Within this report are mandatory questions regarding if a patient is receiving IBR and, if not, why. A retrospective review of this database was performed for all patients undergoing surgical treatment of breast cancer over two years. All statistical comparisons were made using chi-squared test for categorical variables with a p-value of 0.05 considered significant. RESULTS: Of 6253 patients undergoing breast cancer surgery, 2649 underwent mastectomy and 615 mastectomy patients received IBR. The most commonly reported reasons patients did not undergo IBR were patient preference (55%), high likelihood of postoperative radiation therapy (20%), and high risk due to patient co-morbidities (12%). Resource limitations (2%) and a lack of an IBR discussion (3%) was rarely cited as reasons for no IBR. CONCLUSIONS: There are many reconstructive options following mastectomy in breast cancer survivors. This study provides a unique look into general surgeon reported reasons patients are not receiving IBR and demonstrates the need for further probing into the thought-process behind these reported reasons from both a surgeon and patient perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".